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20202024
most citedDemonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach

13 citations · 16 across the 3 of their papers we have counts for

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cs.RO202413 cited

Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach

Max Bajracharya, James Borders, Richard Cheng +11

We present our general-purpose mobile manipulation system consisting of a custom robot platform and key algorithms spanning perception and planning. To extensively test the system…

cs.RO2022

SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception

Kaushik Shivakumar, Vainavi Viswanath, Anrui Gu +6

Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling. This paper extends prior work on autonomously untangling long cables by introducing…

cs.RO20213 cited

Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty

Richard Cheng, Richard M. Murray, Joel W. Burdick

When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Man…

cs.RO2020

Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Richard Cheng, Mohammad Javad Khojasteh, Aaron D. Ames +1

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF…

cs.RO2020

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Maegan Tucker, Myra Cheng, Ellen Novoseller +4

Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space. However, existing preferenc…